1 Análisis PPM-S

library(sjPlot)
library(dplyr)
library(lavaan)

data01 <- sjlabelled::read_spss(path = "data/dat1.sav",verbose = FALSE)

dat01 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv01")) %>% na.omit()
dat02 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv02")) %>% na.omit()
dat03 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv03_p")) %>% na.omit()

1.1 Version 01:

  1. Percepcion esfuerzo
  2. Percepcion talento
  3. Percepcion familia rica
  4. Percepcion redes
  5. Preferencia esfuerzo
  6. Preferencia talento
  7. Preferencia familia rica
  8. Preferencia redes
model01 <- 'perc_merit=~meritv01_perc_effort+meritv01_perc_talent 
            perc_nmerit=~meritv01_perc_wpart+meritv01_perc_netw  
            pref_merit=~meritv01_pref_effort+meritv01_pref_talent 
            pref_nmerit=~meritv01_pref_wpart+meritv01_pref_netw'

fit1 <- cfa(model = model01,data = dat01,ordered = c("meritv01_perc_effort","meritv01_perc_talent",
                                                     "meritv01_perc_wpart","meritv01_perc_netw",
                                                     "meritv01_pref_effort","meritv01_pref_talent",
                                                     "meritv01_pref_wpart","meritv01_pref_netw"))

summary(fit1,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit1,what = "std",thresholds = FALSE, intercepts = FALSE)

## lavaan 0.6-4 ended normally after 41 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                         46
## 
##   Number of observations                           410
## 
##   Estimator                                       DWLS      Robust
##   Model Fit Test Statistic                      25.944      45.923
##   Degrees of freedom                                14          14
##   P-value (Chi-square)                           0.026       0.000
##   Scaling correction factor                                  0.602
##   Shift parameter                                            2.827
##     for simple second-order correction (Mplus variant)
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             3417.782    2335.438
##   Degrees of freedom                                28          28
##   P-value                                        0.000       0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.996       0.986
##   Tucker-Lewis Index (TLI)                       0.993       0.972
## 
##   Robust Comparative Fit Index (CFI)                            NA
##   Robust Tucker-Lewis Index (TLI)                               NA
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.046       0.075
##   90 Percent Confidence Interval          0.015  0.073       0.051  0.099
##   P-value RMSEA <= 0.05                          0.565       0.042
## 
##   Robust RMSEA                                                  NA
##   90 Percent Confidence Interval                                NA     NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.043       0.043
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model        Unstructured
##   Standard Errors                           Robust.sem
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit =~                                                         
##     mrtv01_prc_ffr    1.000                               0.681    0.681
##     mrtv01_prc_tln    1.258    0.148    8.529    0.000    0.857    0.857
##   perc_nmerit =~                                                        
##     mrtv01_prc_wpr    1.000                               0.810    0.810
##     mrtv01_prc_ntw    1.216    0.099   12.329    0.000    0.985    0.985
##   pref_merit =~                                                         
##     mrtv01_prf_ffr    1.000                               0.816    0.816
##     mrtv01_prf_tln    0.779    0.078   10.009    0.000    0.635    0.635
##   pref_nmerit =~                                                        
##     mrtv01_prf_wpr    1.000                               0.735    0.735
##     mrtv01_prf_ntw    1.255    0.299    4.195    0.000    0.922    0.922
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit ~~                                                         
##     perc_nmerit      -0.033    0.033   -1.022    0.307   -0.060   -0.060
##     pref_merit        0.284    0.043    6.630    0.000    0.510    0.510
##     pref_nmerit       0.120    0.039    3.101    0.002    0.239    0.239
##   perc_nmerit ~~                                                        
##     pref_merit        0.330    0.044    7.432    0.000    0.499    0.499
##     pref_nmerit      -0.008    0.035   -0.217    0.828   -0.013   -0.013
##   pref_merit ~~                                                         
##     pref_nmerit       0.108    0.043    2.509    0.012    0.180    0.180
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .mrtv01_prc_ffr    0.000                               0.000    0.000
##    .mrtv01_prc_tln    0.000                               0.000    0.000
##    .mrtv01_prc_wpr    0.000                               0.000    0.000
##    .mrtv01_prc_ntw    0.000                               0.000    0.000
##    .mrtv01_prf_ffr    0.000                               0.000    0.000
##    .mrtv01_prf_tln    0.000                               0.000    0.000
##    .mrtv01_prf_wpr    0.000                               0.000    0.000
##    .mrtv01_prf_ntw    0.000                               0.000    0.000
##     perc_merit        0.000                               0.000    0.000
##     perc_nmerit       0.000                               0.000    0.000
##     pref_merit        0.000                               0.000    0.000
##     pref_nmerit       0.000                               0.000    0.000
## 
## Thresholds:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     mrtv01_prc_f|1   -1.165    0.080  -14.571    0.000   -1.165   -1.165
##     mrtv01_prc_f|2   -0.455    0.064   -7.076    0.000   -0.455   -0.455
##     mrtv01_prc_f|3    0.031    0.062    0.493    0.622    0.031    0.031
##     mrtv01_prc_f|4    0.741    0.069   10.809    0.000    0.741    0.741
##     mrtv01_prc_t|1   -1.254    0.083  -15.041    0.000   -1.254   -1.254
##     mrtv01_prc_t|2   -0.408    0.064   -6.394    0.000   -0.408   -0.408
##     mrtv01_prc_t|3    0.389    0.064    6.100    0.000    0.389    0.389
##     mrtv01_prc_t|4    1.268    0.084   15.102    0.000    1.268    1.268
##     mrtv01_prc_w|1   -1.296    0.085  -15.220    0.000   -1.296   -1.296
##     mrtv01_prc_w|2   -0.868    0.071  -12.183    0.000   -0.868   -0.868
##     mrtv01_prc_w|3   -0.253    0.063   -4.041    0.000   -0.253   -0.253
##     mrtv01_prc_w|4    0.356    0.063    5.611    0.000    0.356    0.356
##     mrtv01_prc_n|1   -1.435    0.092  -15.638    0.000   -1.435   -1.435
##     mrtv01_prc_n|2   -1.042    0.076  -13.727    0.000   -1.042   -1.042
##     mrtv01_prc_n|3   -0.560    0.066   -8.529    0.000   -0.560   -0.560
##     mrtv01_prc_n|4    0.469    0.065    7.271    0.000    0.469    0.469
##     mrtv01_prf_f|1   -1.547    0.098  -15.769    0.000   -1.547   -1.547
##     mrtv01_prf_f|2   -1.165    0.080  -14.571    0.000   -1.165   -1.165
##     mrtv01_prf_f|3   -0.663    0.067   -9.868    0.000   -0.663   -0.663
##     mrtv01_prf_f|4    0.166    0.062    2.662    0.008    0.166    0.166
##     mrtv01_prf_t|1   -1.489    0.095  -15.723    0.000   -1.489   -1.489
##     mrtv01_prf_t|2   -0.790    0.070  -11.365    0.000   -0.790   -0.790
##     mrtv01_prf_t|3    0.141    0.062    2.268    0.023    0.141    0.141
##     mrtv01_prf_t|4    0.914    0.072   12.626    0.000    0.914    0.914
##     mrtv01_prf_w|1   -0.807    0.070  -11.549    0.000   -0.807   -0.807
##     mrtv01_prf_w|2   -0.135    0.062   -2.170    0.030   -0.135   -0.135
##     mrtv01_prf_w|3    0.816    0.070   11.640    0.000    0.816    0.816
##     mrtv01_prf_w|4    1.734    0.111   15.606    0.000    1.734    1.734
##     mrtv01_prf_n|1   -0.663    0.067   -9.868    0.000   -0.663   -0.663
##     mrtv01_prf_n|2    0.086    0.062    1.381    0.167    0.086    0.086
##     mrtv01_prf_n|3    1.052    0.076   13.808    0.000    1.052    1.052
##     mrtv01_prf_n|4    1.971    0.133   14.789    0.000    1.971    1.971
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .mrtv01_prc_ffr    0.536                               0.536    0.536
##    .mrtv01_prc_tln    0.265                               0.265    0.265
##    .mrtv01_prc_wpr    0.344                               0.344    0.344
##    .mrtv01_prc_ntw    0.030                               0.030    0.030
##    .mrtv01_prf_ffr    0.335                               0.335    0.335
##    .mrtv01_prf_tln    0.596                               0.596    0.596
##    .mrtv01_prf_wpr    0.460                               0.460    0.460
##    .mrtv01_prf_ntw    0.150                               0.150    0.150
##     perc_merit        0.464    0.064    7.201    0.000    1.000    1.000
##     perc_nmerit       0.656    0.059   11.060    0.000    1.000    1.000
##     pref_merit        0.665    0.072    9.221    0.000    1.000    1.000
##     pref_nmerit       0.540    0.131    4.127    0.000    1.000    1.000
## 
## Scales y*:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     mrtv01_prc_ffr    1.000                               1.000    1.000
##     mrtv01_prc_tln    1.000                               1.000    1.000
##     mrtv01_prc_wpr    1.000                               1.000    1.000
##     mrtv01_prc_ntw    1.000                               1.000    1.000
##     mrtv01_prf_ffr    1.000                               1.000    1.000
##     mrtv01_prf_tln    1.000                               1.000    1.000
##     mrtv01_prf_wpr    1.000                               1.000    1.000
##     mrtv01_prf_ntw    1.000                               1.000    1.000

1.2 Version 02:

  1. Percepcion esfuerzo
  2. Preferencia esfuerzo
  3. Percepcion talento
  4. Preferencia talento
  5. Percepcion familia rica
  6. Preferencia familia rica
  7. Percepcion redes
  8. Preferencia redes
model02 <- 'perc_merit=~meritv02_perc_effort+meritv02_perc_talent 
            perc_nmerit=~meritv02_perc_wpart+meritv02_perc_netw  
            pref_merit=~meritv02_pref_effort+meritv02_pref_talent 
            pref_nmerit=~meritv02_pref_wpart+meritv02_pref_netw'

fit2 <- cfa(model = model02,data = dat02,ordered = c("meritv02_perc_effort","meritv02_perc_talent",
                                                     "meritv02_perc_wpart","meritv02_perc_netw",
                                                     "meritv02_pref_effort","meritv02_pref_talent",
                                                     "meritv02_pref_wpart","meritv02_pref_netw"))
## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative
summary(fit2,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit2,what = "std")

## lavaan 0.6-4 ended normally after 37 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                         46
## 
##   Number of observations                           412
## 
##   Estimator                                       DWLS      Robust
##   Model Fit Test Statistic                      49.320      69.598
##   Degrees of freedom                                14          14
##   P-value (Chi-square)                           0.000       0.000
##   Scaling correction factor                                  0.728
##   Shift parameter                                            1.866
##     for simple second-order correction (Mplus variant)
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             1565.199    1156.514
##   Degrees of freedom                                28          28
##   P-value                                        0.000       0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.977       0.951
##   Tucker-Lewis Index (TLI)                       0.954       0.901
## 
##   Robust Comparative Fit Index (CFI)                            NA
##   Robust Tucker-Lewis Index (TLI)                               NA
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.078       0.098
##   90 Percent Confidence Interval          0.055  0.103       0.076  0.122
##   P-value RMSEA <= 0.05                          0.023       0.000
## 
##   Robust RMSEA                                                  NA
##   90 Percent Confidence Interval                                NA     NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.057       0.057
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model        Unstructured
##   Standard Errors                           Robust.sem
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit =~                                                         
##     mrtv02_prc_ffr    1.000                               0.809    0.809
##     mrtv02_prc_tln    0.815    0.075   10.828    0.000    0.660    0.660
##   perc_nmerit =~                                                        
##     mrtv02_prc_wpr    1.000                               0.790    0.790
##     mrtv02_prc_ntw    1.020    0.115    8.851    0.000    0.806    0.806
##   pref_merit =~                                                         
##     mrtv02_prf_ffr    1.000                               0.828    0.828
##     mrtv02_prf_tln    0.724    0.065   11.056    0.000    0.599    0.599
##   pref_nmerit =~                                                        
##     mrtv02_prf_wpr    1.000                               1.285    1.285
##     mrtv02_prf_ntw    0.286    0.197    1.457    0.145    0.368    0.368
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit ~~                                                         
##     perc_nmerit       0.007    0.041    0.162    0.871    0.010    0.010
##     pref_merit        0.457    0.039   11.794    0.000    0.682    0.682
##     pref_nmerit       0.180    0.050    3.561    0.000    0.173    0.173
##   perc_nmerit ~~                                                        
##     pref_merit        0.335    0.045    7.371    0.000    0.512    0.512
##     pref_nmerit       0.164    0.045    3.649    0.000    0.161    0.161
##   pref_merit ~~                                                         
##     pref_nmerit       0.081    0.049    1.657    0.097    0.077    0.077
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .mrtv02_prc_ffr    0.000                               0.000    0.000
##    .mrtv02_prc_tln    0.000                               0.000    0.000
##    .mrtv02_prc_wpr    0.000                               0.000    0.000
##    .mrtv02_prc_ntw    0.000                               0.000    0.000
##    .mrtv02_prf_ffr    0.000                               0.000    0.000
##    .mrtv02_prf_tln    0.000                               0.000    0.000
##    .mrtv02_prf_wpr    0.000                               0.000    0.000
##    .mrtv02_prf_ntw    0.000                               0.000    0.000
##     perc_merit        0.000                               0.000    0.000
##     perc_nmerit       0.000                               0.000    0.000
##     pref_merit        0.000                               0.000    0.000
##     pref_nmerit       0.000                               0.000    0.000
## 
## Thresholds:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     mrtv02_prc_f|1   -0.917    0.072  -12.689    0.000   -0.917   -0.917
##     mrtv02_prc_f|2   -0.315    0.063   -5.011    0.000   -0.315   -0.315
##     mrtv02_prc_f|3    0.073    0.062    1.181    0.238    0.073    0.073
##     mrtv02_prc_f|4    0.761    0.069   11.064    0.000    0.761    0.761
##     mrtv02_prc_t|1   -1.298    0.085  -15.268    0.000   -1.298   -1.298
##     mrtv02_prc_t|2   -0.487    0.065   -7.544    0.000   -0.487   -0.487
##     mrtv02_prc_t|3    0.233    0.062    3.736    0.000    0.233    0.233
##     mrtv02_prc_t|4    1.327    0.086   15.377    0.000    1.327    1.327
##     mrtv02_prc_w|1   -1.218    0.082  -14.899    0.000   -1.218   -1.218
##     mrtv02_prc_w|2   -0.828    0.070  -11.797    0.000   -0.828   -0.828
##     mrtv02_prc_w|3   -0.387    0.064   -6.086    0.000   -0.387   -0.387
##     mrtv02_prc_w|4    0.348    0.063    5.500    0.000    0.348    0.348
##     mrtv02_prc_n|1   -1.455    0.093  -15.713    0.000   -1.455   -1.455
##     mrtv02_prc_n|2   -0.964    0.073  -13.120    0.000   -0.964   -0.964
##     mrtv02_prc_n|3   -0.460    0.064   -7.156    0.000   -0.460   -0.460
##     mrtv02_prc_n|4    0.593    0.066    8.989    0.000    0.593    0.593
##     mrtv02_prf_f|1   -1.156    0.079  -14.552    0.000   -1.156   -1.156
##     mrtv02_prf_f|2   -0.964    0.073  -13.120    0.000   -0.964   -0.964
##     mrtv02_prf_f|3   -0.593    0.066   -8.989    0.000   -0.593   -0.593
##     mrtv02_prf_f|4    0.303    0.063    4.815    0.000    0.303    0.303
##     mrtv02_prf_t|1   -1.591    0.101  -15.812    0.000   -1.591   -1.591
##     mrtv02_prf_t|2   -0.721    0.068  -10.599    0.000   -0.721   -0.721
##     mrtv02_prf_t|3    0.196    0.062    3.147    0.002    0.196    0.196
##     mrtv02_prf_t|4    1.110    0.078   14.256    0.000    1.110    1.110
##     mrtv02_prf_w|1   -0.854    0.071  -12.068    0.000   -0.854   -0.854
##     mrtv02_prf_w|2   -0.328    0.063   -5.207    0.000   -0.328   -0.328
##     mrtv02_prf_w|3    0.557    0.065    8.509    0.000    0.557    0.557
##     mrtv02_prf_w|4    1.591    0.101   15.812    0.000    1.591    1.591
##     mrtv02_prf_n|1   -0.721    0.068  -10.599    0.000   -0.721   -0.721
##     mrtv02_prf_n|2    0.097    0.062    1.574    0.115    0.097    0.097
##     mrtv02_prf_n|3    1.004    0.075   13.457    0.000    1.004    1.004
##     mrtv02_prf_n|4    1.858    0.122   15.291    0.000    1.858    1.858
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .mrtv02_prc_ffr    0.345                               0.345    0.345
##    .mrtv02_prc_tln    0.565                               0.565    0.565
##    .mrtv02_prc_wpr    0.376                               0.376    0.376
##    .mrtv02_prc_ntw    0.351                               0.351    0.351
##    .mrtv02_prf_ffr    0.314                               0.314    0.314
##    .mrtv02_prf_tln    0.641                               0.641    0.641
##    .mrtv02_prf_wpr   -0.651                              -0.651   -0.651
##    .mrtv02_prf_ntw    0.865                               0.865    0.865
##     perc_merit        0.655    0.070    9.336    0.000    1.000    1.000
##     perc_nmerit       0.624    0.079    7.917    0.000    1.000    1.000
##     pref_merit        0.686    0.072    9.561    0.000    1.000    1.000
##     pref_nmerit       1.651    1.116    1.479    0.139    1.000    1.000
## 
## Scales y*:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     mrtv02_prc_ffr    1.000                               1.000    1.000
##     mrtv02_prc_tln    1.000                               1.000    1.000
##     mrtv02_prc_wpr    1.000                               1.000    1.000
##     mrtv02_prc_ntw    1.000                               1.000    1.000
##     mrtv02_prf_ffr    1.000                               1.000    1.000
##     mrtv02_prf_tln    1.000                               1.000    1.000
##     mrtv02_prf_wpr    1.000                               1.000    1.000
##     mrtv02_prf_ntw    1.000                               1.000    1.000

1.3 Version 03: orden aleatorio

model03 <- 'perc_merit=~meritv03_perc_effort+meritv03_perc_talent 
            perc_nmerit=~meritv03_perc_wpart+meritv03_perc_netw  
            pref_merit=~meritv03_pref_effort+meritv03_pref_talent 
            pref_nmerit=~meritv03_pref_wpart+meritv03_pref_netw'

fit3 <- cfa(model = model03,data = dat03,ordered = c("meritv03_perc_effort","meritv03_perc_talent",
                                                     "meritv03_perc_wpart","meritv03_perc_netw",
                                                     "meritv03_pref_effort","meritv03_pref_talent",
                                                     "meritv03_pref_wpart","meritv03_pref_netw"))

summary(fit3,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit3,what = "std",thresholds = FALSE, intercepts = FALSE)

## lavaan 0.6-4 ended normally after 39 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                         46
## 
##   Number of observations                           410
## 
##   Estimator                                       DWLS      Robust
##   Model Fit Test Statistic                      25.502      39.240
##   Degrees of freedom                                14          14
##   P-value (Chi-square)                           0.030       0.000
##   Scaling correction factor                                  0.692
##   Shift parameter                                            2.374
##     for simple second-order correction (Mplus variant)
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             1746.673    1316.959
##   Degrees of freedom                                28          28
##   P-value                                        0.000       0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.993       0.980
##   Tucker-Lewis Index (TLI)                       0.987       0.961
## 
##   Robust Comparative Fit Index (CFI)                            NA
##   Robust Tucker-Lewis Index (TLI)                               NA
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.045       0.066
##   90 Percent Confidence Interval          0.014  0.072       0.042  0.091
##   P-value RMSEA <= 0.05                          0.585       0.123
## 
##   Robust RMSEA                                                  NA
##   90 Percent Confidence Interval                                NA     NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.043       0.043
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model        Unstructured
##   Standard Errors                           Robust.sem
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit =~                                                         
##     mrtv03_prc_ffr    1.000                               0.655    0.655
##     mrtv03_prc_tln    1.060    0.189    5.610    0.000    0.694    0.694
##   perc_nmerit =~                                                        
##     mrtv03_prc_wpr    1.000                               0.793    0.793
##     mrtv03_prc_ntw    1.145    0.133    8.637    0.000    0.908    0.908
##   pref_merit =~                                                         
##     mrtv03_prf_ffr    1.000                               0.731    0.731
##     mrtv03_prf_tln    0.847    0.110    7.719    0.000    0.619    0.619
##   pref_nmerit =~                                                        
##     mrtv03_prf_wpr    1.000                               0.602    0.602
##     mrtv03_prf_ntw    1.575    0.340    4.632    0.000    0.949    0.949
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit ~~                                                         
##     perc_nmerit      -0.032    0.035   -0.899    0.369   -0.061   -0.061
##     pref_merit        0.205    0.042    4.930    0.000    0.429    0.429
##     pref_nmerit       0.154    0.041    3.753    0.000    0.389    0.389
##   perc_nmerit ~~                                                        
##     pref_merit        0.327    0.046    7.125    0.000    0.564    0.564
##     pref_nmerit      -0.060    0.031   -1.963    0.050   -0.126   -0.126
##   pref_merit ~~                                                         
##     pref_nmerit       0.044    0.034    1.290    0.197    0.099    0.099
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .mrtv03_prc_ffr    0.000                               0.000    0.000
##    .mrtv03_prc_tln    0.000                               0.000    0.000
##    .mrtv03_prc_wpr    0.000                               0.000    0.000
##    .mrtv03_prc_ntw    0.000                               0.000    0.000
##    .mrtv03_prf_ffr    0.000                               0.000    0.000
##    .mrtv03_prf_tln    0.000                               0.000    0.000
##    .mrtv03_prf_wpr    0.000                               0.000    0.000
##    .mrtv03_prf_ntw    0.000                               0.000    0.000
##     perc_merit        0.000                               0.000    0.000
##     perc_nmerit       0.000                               0.000    0.000
##     pref_merit        0.000                               0.000    0.000
##     pref_nmerit       0.000                               0.000    0.000
## 
## Thresholds:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     mrtv03_prc_f|1   -1.228    0.082  -14.913    0.000   -1.228   -1.228
##     mrtv03_prc_f|2   -0.497    0.065   -7.659    0.000   -0.497   -0.497
##     mrtv03_prc_f|3   -0.160    0.062   -2.564    0.010   -0.160   -0.160
##     mrtv03_prc_f|4    0.546    0.065    8.336    0.000    0.546    0.546
##     mrtv03_prc_t|1   -1.282    0.085  -15.162    0.000   -1.282   -1.282
##     mrtv03_prc_t|2   -0.442    0.064   -6.881    0.000   -0.442   -0.442
##     mrtv03_prc_t|3    0.141    0.062    2.268    0.023    0.141    0.141
##     mrtv03_prc_t|4    0.932    0.073   12.801    0.000    0.932    0.932
##     mrtv03_prc_w|1   -1.268    0.084  -15.102    0.000   -1.268   -1.268
##     mrtv03_prc_w|2   -0.757    0.069  -10.996    0.000   -0.757   -0.757
##     mrtv03_prc_w|3   -0.402    0.064   -6.296    0.000   -0.402   -0.402
##     mrtv03_prc_w|4    0.298    0.063    4.728    0.000    0.298    0.298
##     mrtv03_prc_n|1   -1.453    0.093  -15.670    0.000   -1.453   -1.453
##     mrtv03_prc_n|2   -1.031    0.076  -13.646    0.000   -1.031   -1.031
##     mrtv03_prc_n|3   -0.596    0.066   -9.010    0.000   -0.596   -0.596
##     mrtv03_prc_n|4    0.203    0.062    3.253    0.001    0.203    0.203
##     mrtv03_prf_f|1   -1.489    0.095  -15.723    0.000   -1.489   -1.489
##     mrtv03_prf_f|2   -1.268    0.084  -15.102    0.000   -1.268   -1.268
##     mrtv03_prf_f|3   -0.807    0.070  -11.549    0.000   -0.807   -0.807
##     mrtv03_prf_f|4    0.012    0.062    0.197    0.844    0.012    0.012
##     mrtv03_prf_t|1   -1.402    0.090  -15.564    0.000   -1.402   -1.402
##     mrtv03_prf_t|2   -0.749    0.069  -10.903    0.000   -0.749   -0.749
##     mrtv03_prf_t|3   -0.031    0.062   -0.493    0.622   -0.031   -0.031
##     mrtv03_prf_t|4    0.663    0.067    9.868    0.000    0.663    0.663
##     mrtv03_prf_w|1   -0.895    0.072  -12.450    0.000   -0.895   -0.895
##     mrtv03_prf_w|2   -0.247    0.063   -3.942    0.000   -0.247   -0.247
##     mrtv03_prf_w|3    0.449    0.064    6.979    0.000    0.449    0.449
##     mrtv03_prf_w|4    1.310    0.086   15.275    0.000    1.310    1.310
##     mrtv03_prf_n|1   -0.757    0.069  -10.996    0.000   -0.757   -0.757
##     mrtv03_prf_n|2    0.055    0.062    0.888    0.375    0.055    0.055
##     mrtv03_prf_n|3    0.694    0.068   10.247    0.000    0.694    0.694
##     mrtv03_prf_n|4    1.567    0.099   15.774    0.000    1.567    1.567
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .mrtv03_prc_ffr    0.571                               0.571    0.571
##    .mrtv03_prc_tln    0.518                               0.518    0.518
##    .mrtv03_prc_wpr    0.371                               0.371    0.371
##    .mrtv03_prc_ntw    0.175                               0.175    0.175
##    .mrtv03_prf_ffr    0.465                               0.465    0.465
##    .mrtv03_prf_tln    0.616                               0.616    0.616
##    .mrtv03_prf_wpr    0.637                               0.637    0.637
##    .mrtv03_prf_ntw    0.100                               0.100    0.100
##     perc_merit        0.429    0.084    5.135    0.000    1.000    1.000
##     perc_nmerit       0.629    0.079    7.939    0.000    1.000    1.000
##     pref_merit        0.535    0.082    6.548    0.000    1.000    1.000
##     pref_nmerit       0.363    0.085    4.290    0.000    1.000    1.000
## 
## Scales y*:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     mrtv03_prc_ffr    1.000                               1.000    1.000
##     mrtv03_prc_tln    1.000                               1.000    1.000
##     mrtv03_prc_wpr    1.000                               1.000    1.000
##     mrtv03_prc_ntw    1.000                               1.000    1.000
##     mrtv03_prf_ffr    1.000                               1.000    1.000
##     mrtv03_prf_tln    1.000                               1.000    1.000
##     mrtv03_prf_wpr    1.000                               1.000    1.000
##     mrtv03_prf_ntw    1.000                               1.000    1.000

1.4 Version 04: muestra completa

dat04 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv01"),starts_with("meritv02"),starts_with("meritv03_p"))

dat04$perc_effort <- rowSums(dat04[,c(matches(match = "perc_effort",vars = names(dat04)))],na.rm = TRUE)
dat04$perc_talent <- rowSums(dat04[,c(matches(match = "perc_talent",vars = names(dat04)))],na.rm = TRUE)
dat04$perc_wpart  <- rowSums(dat04[,c(matches(match = "perc_wpart" ,vars = names(dat04)))],na.rm = TRUE)
dat04$perc_netw   <- rowSums(dat04[,c(matches(match = "perc_netw"  ,vars = names(dat04)))],na.rm = TRUE)

dat04$pref_effort <- rowSums(dat04[,c(matches(match = "pref_effort",vars = names(dat04)))],na.rm = TRUE)
dat04$pref_talent <- rowSums(dat04[,c(matches(match = "pref_talent",vars = names(dat04)))],na.rm = TRUE)
dat04$pref_wpart  <- rowSums(dat04[,c(matches(match = "pref_wpart" ,vars = names(dat04)))],na.rm = TRUE)
dat04$pref_netw   <- rowSums(dat04[,c(matches(match = "pref_netw"  ,vars = names(dat04)))],na.rm = TRUE)

model04 <- 'perc_merit=~perc_effort+perc_talent 
            perc_nmerit=~perc_wpart+perc_netw  
            pref_merit=~pref_effort+pref_talent 
            pref_nmerit=~pref_wpart+pref_netw'

fit4 <- cfa(model = model04,data = dat04)
summary(fit4,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit4,what = "std",thresholds = FALSE, intercepts = FALSE)

## lavaan 0.6-4 ended normally after 46 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                         22
## 
##   Number of observations                          1234
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                     152.731
##   Degrees of freedom                                14
##   P-value (Chi-square)                           0.000
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             2175.728
##   Degrees of freedom                                28
##   P-value                                        0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.935
##   Tucker-Lewis Index (TLI)                       0.871
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)             -14994.788
##   Loglikelihood unrestricted model (H1)     -14918.422
## 
##   Number of free parameters                         22
##   Akaike (AIC)                               30033.576
##   Bayesian (BIC)                             30146.172
##   Sample-size adjusted Bayesian (BIC)        30076.290
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.090
##   90 Percent Confidence Interval          0.077  0.103
##   P-value RMSEA <= 0.05                          0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.044
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model          Structured
##   Standard Errors                             Standard
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit =~                                                         
##     perc_effort       1.000                               1.034    0.744
##     perc_talent       0.721    0.061   11.840    0.000    0.745    0.629
##   perc_nmerit =~                                                        
##     perc_wpart        1.000                               0.976    0.727
##     perc_netw         1.081    0.071   15.129    0.000    1.055    0.874
##   pref_merit =~                                                         
##     pref_effort       1.000                               0.955    0.774
##     pref_talent       0.658    0.051   12.999    0.000    0.629    0.542
##   pref_nmerit =~                                                        
##     pref_wpart        1.000                               0.737    0.622
##     pref_netw         1.263    0.210    6.018    0.000    0.931    0.833
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   perc_merit ~~                                                         
##     perc_nmerit      -0.001    0.039   -0.030    0.976   -0.001   -0.001
##     pref_merit        0.515    0.048   10.621    0.000    0.522    0.522
##     pref_nmerit       0.229    0.044    5.196    0.000    0.300    0.300
##   perc_nmerit ~~                                                        
##     pref_merit        0.509    0.047   10.798    0.000    0.546    0.546
##     pref_nmerit       0.012    0.026    0.459    0.646    0.017    0.017
##   pref_merit ~~                                                         
##     pref_nmerit       0.066    0.030    2.204    0.028    0.094    0.094
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .perc_effort       0.860    0.091    9.428    0.000    0.860    0.446
##    .perc_talent       0.849    0.056   15.280    0.000    0.849    0.605
##    .perc_wpart        0.848    0.067   12.719    0.000    0.848    0.471
##    .perc_netw         0.345    0.068    5.044    0.000    0.345    0.237
##    .pref_effort       0.608    0.067    9.038    0.000    0.608    0.400
##    .pref_talent       0.952    0.047   20.267    0.000    0.952    0.707
##    .pref_wpart        0.861    0.095    9.053    0.000    0.861    0.613
##    .pref_netw         0.382    0.142    2.691    0.007    0.382    0.306
##     perc_merit        1.068    0.109    9.773    0.000    1.000    1.000
##     perc_nmerit       0.952    0.086   11.092    0.000    1.000    1.000
##     pref_merit        0.912    0.084   10.840    0.000    1.000    1.000
##     pref_nmerit       0.544    0.099    5.481    0.000    1.000    1.000